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赵鹏程
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Personal Information
  • Supervisor of Master's Candidates
  • Name (Pinyin):Zhao Pengcheng
  • Date of Birth:1993-09-05
  • E-Mail:
  • Date of Employment:2019-12-07
  • Administrative Position:高级实验师
  • Education Level:With Certificate of Graduation for Doctorate Study
  • Business Address:武汉大学信息学部遥感信息工程学院（5号楼）315办公室
  • Gender:Male
  • Contact Information:+86 15972003670
  • Status:Employed
  • Alma Mater:武汉大学
  • Teacher College:School of Remote Sensing and Information Engineering
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Paper Publications
Current position: Home   >   Scientific Research   >   Paper Publications

Weakly supervised 3D point cloud semantic segmentation for architectural heritage using teacher-guided consistency and contrast learning

  • Date of Publication:2025-01-07
  • Hits:

  • DOI number: 

    10.1016/j.autcon.2024.105831

  • Affiliation of Author(s): 

    School of Remote Sensing and Information Engineering, Wuhan University, China

  • Journal: 

    AUTOMATION IN CONSTRUCTION

  • Key Words: 

    Point cloud,Architectural heritage,3D semantic segmentation,Weakly supervised

  • Abstract: 

    Point cloud semantic segmentation is significant for managing and protecting architectural heritage. Currently, fully supervised methods require a large amount of annotated data, while weakly supervised methods are difficult to transfer directly to architectural heritage. This paper proposes an end-to-end teacher-guided consistency and contrastive learning weakly supervised (TCCWS) framework for architectural heritage point cloud semantic segmentation, which can fully utilize limited labeled points to train network. Specifically, a teacherstudent framework is designed to generate pseudo labels and a pseudo label dividing module is proposed to distinguish reliable and ambiguous point sets. Based on it, a consistency and contrastive learning strategy is designed to fully utilize supervision signals to learn the features of point clouds. The framework is tested on the ArCH dataset and self-collected point cloud, which demonstrates that the proposed method can achieve effective semantic segmentation of architectural heritage using only 0.1 % of annotated points.

  • Co-author: 

    Pengcheng,Zhao, Mingyao,Ai, Qingwu, Shuowen,Huang, Shaohua,Wang, Hao,Cui, Jian,Li,Hu

  • Indexed by: 

    Journal paper

  • Discipline: 

    Engineering

  • Document Type: 

    J

  • Volume: 

    168

  • ISSN No.: 

    0926-5805

  • Translation or Not: 

    no

  • CN No.: 

    EI:20244217207097,WOS:001337166400001,Scopus:2-s2.0-85206269815

  • Date of Publication: 

    2024-12-01

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